#data-pipeline

#data-pipeline

Dynamic transformations enable flexible schema adaptations without code changes.

Using schema metadata simplifies column management, renaming, and casting.

Build a defensive Spark ETL pipeline to ensure robust data processing.

Handle data issues like schema mismatches and corrupt records effectively.

Implement custom logging and audit trails for better failure management.

Date and time operations are vital for analysis in various sectors, enabling insights into trends and customer behavior.

Cloud pricing involves almost 5 million SKUs across major providers, necessitating a robust data pipeline for accurate estimates.

A data pipeline using Google Cloud services and LangChain efficiently indexes document embeddings into Redis, supporting RAG-based question-answering systems.

Data observability is critical for accurate invoicing in consumption-based pricing models.

Canva chose Amazon KDS over other solutions for its Product Analytics Platform due to lower costs and high performance requirements.